Paper Citation Record · LEDGER

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics

As of 14 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2502.01867.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.01867 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:18:23.564456Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-07T16:29:11.182014Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T23:41:17.164247Z

Reference resolution

36 of 36 outbound references displayed

verified exact1
verified fuzzy33
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a3558298-1e59-487e-a713-4b8316793c51 · outbound

This paper cites Wide & deep learning for recommender systems.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Wide & deep learning for recommender systems

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.931283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7b271130-9fc1-4739-aa31-238e5b69fbfa · outbound

This paper cites Cold start to improve market thickness on online advertising platforms: Data-driven algorithms and field experiments.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Cold start to improve market thickness on online advertising platforms: Data-driven algorithms and field experiments

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.657856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e1ea69c0-59ea-4bc9-bb83-8aed84872fcb · outbound

This paper cites Finite-time analysis of the multiarmed bandit problem.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Finite-time analysis of the multiarmed bandit problem

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.619815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b8080cb0-9680-401d-8cb0-eb3f68033dc4 · outbound

This paper cites Bandits and recom- mender systems.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Bandits and recom- mender systems

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.525972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fa32ea3f-f639-4482-a90d-6fecea5cc366 · outbound

This paper cites Social learning in multi agent multi armed bandits.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Social learning in multi agent multi armed bandits

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.412540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e592d450-38b5-475a-846e-b90cdde411b3 · outbound

This paper cites An experimental comparison of click position-bias models.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics An experimental comparison of click position-bias models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.228333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f7bad5c3-ad63-45b7-aa9f-5440fb1db58e · outbound

This paper cites Multiple-play bandits in the position-based model.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Multiple-play bandits in the position-based model

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.107721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 55739646-b9c1-4512-9aab-6cd1c5fcb447 · outbound

This paper cites Regression-based latent factor models.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Regression-based latent factor models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.100875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 93e8954e-ce9f-4ca8-8eae-6c464eb46ff9 · outbound

This paper cites Content-based recommender systems: State of the art and trends.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Content-based recommender systems: State of the art and trends

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.086967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b72e1394-d9ac-4418-8e84-dda8cb48fe57 · outbound

This paper cites Col- laborative filtering and deep learning based hybrid recommendation for cold start problem.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Col- laborative filtering and deep learning based hybrid recommendation for cold start problem

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.079298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0dbca5cc-fc88-4d07-8781-9ee40c023ab3 · outbound

This paper cites A meta-learning perspective on cold-start recommendations for items.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics A meta-learning perspective on cold-start recommendations for items

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.071323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7e5e34c9-9a46-4eff-9441-6fb1b1b4ba27 · outbound

This paper cites Cold-start sequential recommendation via meta learner.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Cold-start sequential recommendation via meta learner

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.063946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5eab7785-25f2-4720-9a9d-3826374a2eaa · outbound

This paper cites Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.021471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ca9194f6-5b43-4728-86ae-ec6a76f78257 · outbound

This paper cites Using confidence bounds for exploitation-exploration trade-offs.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Using confidence bounds for exploitation-exploration trade-offs

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:23.961903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 82d450f2-09af-4094-89ac-00e370919cdc · outbound

This paper cites An empirical evaluation of thompson sampling.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics An empirical evaluation of thompson sampling

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:23.832746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7a38fc8e-6a71-4902-b91d-4edb4c805bc9 · outbound

This paper cites Accurately interpreting clickthrough data as implicit feedback.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Accurately interpreting clickthrough data as implicit feedback

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:23.685436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 26294bab-7386-4be1-a862-b64f4ed4a4ab · outbound

This paper cites Click models for web search.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Click models for web search

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:23.678100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9faaec3f-31ba-488a-aaec-732688f0938a · outbound

This paper cites A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:23.670815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 458f7407-8e33-44e1-87b8-2572601e614a · outbound

This paper cites Tight regret bounds for stochastic combinatorial semi-bandits.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Tight regret bounds for stochastic combinatorial semi-bandits

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:23.642150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eafff1b9-6ed2-47c9-b5d4-bdae58edc485 · outbound

This paper cites We denote by Bt,k = q δ ln t Nk(t) the UCB-exploration bonus and by B+ t,k = q δ ln T Nk(t) an upper bound of this bonus.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics We denote by Bt,k = q δ ln t Nk(t) the UCB-exploration bonus and by B+ t,k = q δ ln T Nk(t) an upper bound of this bonus

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:23.634365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 19480e6b-e5c5-4c91-921f-bf2bc02eb0ea · outbound

This paper cites an unresolved cited work.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:18:23.625921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a19ec869-ca52-487e-871f-8474441be3f6 · outbound

This paper cites Probability inequalities for sums of bounded random variables.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Probability inequalities for sums of bounded random variables

Reference 1952

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:23.662435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 73b395d3-f2b4-48ca-b1c6-6a56cc2f2601 · outbound

This paper cites Combinatorial bandits revisited.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Combinatorial bandits revisited

Reference 1994

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:23.649939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0624169d-65e5-4d00-929f-36365f8c9417 · outbound

This paper cites Thompson sampling for dynamic multi-armed bandits.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Thompson sampling for dynamic multi-armed bandits

Reference 2002

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.604274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4670cf1b-b8dc-437d-a1fa-438cd9342e08 · outbound

This paper cites Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments

Reference 2008

Resolution: verified exact; local_arxiv, observed 2026-08-09T14:18:23.601698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 830af670-11aa-4342-95ed-b353f5bd1701 · outbound

This paper cites Factorization meets the neighborhood: a multifaceted collaborative filtering model.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Factorization meets the neighborhood: a multifaceted collaborative filtering model

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.093985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 473efd42-1fee-4726-9046-e894cc4f57c3 · outbound

This paper cites Improved online learning algorithms for ctr prediction in ad auctions.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Improved online learning algorithms for ctr prediction in ad auctions

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.567736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bc19ce40-7017-4c9d-99e8-6792608bd785 · outbound

This paper cites Introduction to multi-armed bandits.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Introduction to multi-armed bandits

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.627834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5d6cc94e-3e50-4e89-8256-5c22bace46ce · outbound

This paper cites Multi-armed bandits in recommendation systems: A survey of the state-of-the-art and future directions.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Multi-armed bandits in recommendation systems: A survey of the state-of-the-art and future directions

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.483135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f167f84d-7a58-4a15-b220-33ea73c2b4e9 · outbound

This paper cites Deep & cross network for ad click predictions.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Deep & cross network for ad click predictions

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.820873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 495641cc-d212-49de-a21f-fa4d5e0ecff3 · outbound

This paper cites Deep interest network for click- through rate prediction.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Deep interest network for click- through rate prediction

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.699500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f28c5e6e-ba1c-4670-9747-4b5f83c17533 · outbound

This paper cites DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-09T14:18:22.954875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 339a6943-3bcd-4869-b28a-d77b168e32b0 · outbound

This paper cites Addressing cold start in product search via empirical bayes.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Addressing cold start in product search via empirical bayes

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.642692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8d317ffa-2dee-4f20-8dfb-f987bba3d75f · outbound

This paper cites A perspective view and survey of meta-learning.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics A perspective view and survey of meta-learning

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.056326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d9337fa8-b3fe-4397-a0ec-e642d4269dee · outbound

This paper cites Facing the cold start problem in recommender systems.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Facing the cold start problem in recommender systems

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.635288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 00f71e0e-8c76-43be-b650-a78ebf351e55 · outbound

This paper cites Addressing the item cold-start problem by attribute-driven active learning.

Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics Addressing the item cold-start problem by attribute-driven active learning

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:24.650221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation ae14820a-7ad8-4b61-899c-20ebdcac5fa8 · inbound

Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making cites this paper.

Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:41:17.169867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.